Generative artificial intelligence and non-pharmacological bias: an experimental study on cancer patient sexual health communications.

Objectives The objective of this study was to explore the feature of generative artificial intelligence (AI) in asking sexual health among cancer survivors, which are often challenging for patients to discuss.Methods We employed the Generative Pre-trained Transformer-3.5 (GPT) as the generative AI platform and used DocsBot for citation retrieval (June 2023). A structured prompt was devised to generate 100 questions from the AI, based on epidemiological survey data regarding sexual difficulties among cancer survivors. These questions were submitted to Bot1 (standard GPT) and Bot2 (sourced from two clinical guidelines).Results No censorship of sexual expressions or medical terms occurred. Despite the lack of reflection on guideline recommendations, 'consultation' was significantly more prevalent in both bots' responses compared with pharmacological interventions, with ORs of 47.3 (p<0.001) in Bot1 and 97.2 (p<0.001) in Bot2.Discussion Generative AI can serve to provide health information on sensitive topics such as sexual health, despite the potential for policy-restricted content. Responses were biased towards non-pharmacological interventions, which is probably due to a GPT model designed with the 's prohibition policy on replying to medical topics. This shift warrants attention as it could potentially trigger patients' expectations for non-pharmacological interventions.

Sodium-glucose co-transporter 2 inhibitor therapy reduces the administration frequency of anti-vascular endothelial growth factor agents in patients with diabetic macular oedema with a history of anti-vascular endothelial growth factor agent use: A cohort study using the Japanese health insurance claims database.

AIM: We assessed the effectiveness of sodium-glucose co-transporter 2 inhibitors (SGLT2is) in reducing the administration frequency of anti-vascular endothelial growth factor (VEGF) agents in patients with diabetic macular oedema (DMO) using a health insurance claims database. MATERIALS AND METHODS: This retrospective cohort study analysed health insurance claims data covering 11 million Japanese patients between 2005 and 2019. We analysed the frequency and duration of intravitreal injection of anti-VEGF agents after initiating SGLT2is or other antidiabetic drugs. RESULTS: Among 2412 matched patients with DMO, the incidence rates of anti-VEGF agent injections were 230.1 per 1000 person-year in SGLT2i users and 228.4 times per 1000 person-year in non-users, respectively, and the risk ratio for events was unchanged in both groups. Sub-analysis of each baseline characteristic of the patients showed that SGLT2is were particularly effective in patients with a history of anti-VEGF agent use [p = .027, hazard ratio (HR): 0.44, 95% confidence interval (CI): 0.22-0.91]. SGLT2is reduced the risk for the first (p = .023, HR: 0.45, 95% CI: 0.22-0.91) and second (p = .021, HR: 0.39, 95% CI: 0.17-0.89) anti-VEGF agent injections. CONCLUSIONS: There was no difference in the risk ratio for the addition of anti-VEGF therapy between the two treatment groups. However, the use of SGLT2is reduced the frequency of anti-VEGF agent administration in patients with DMO requiring anti-VEGF therapy. Therefore, SGLT2i therapy may be a novel, non-invasive, low-cost adjunctive therapy for DMO requiring anti-VEGF therapy.

Synovial Tissue Heterogeneity in Japanese Patients With Rheumatoid Arthritis Elucidated Using a Cell-Type Deconvolution Approach.

OBJECTIVE: Recent advances in single-cell RNA sequencing technology have improved our understanding of the immunological landscape of rheumatoid arthritis (RA). We aimed to stratify the synovium from East Asian patients with RA by immune cell compositions and gain insight into the inflammatory drivers of each synovial phenotype. METHODS: Synovial tissues were obtained from East Asian patients in Japan with RA (n = 41) undergoing articular surgery. The cellular composition was quantified by a deconvolution approach using a public single-cell-based reference. Inflammatory pathway activity was calculated by gene set variation analysis, and chromatin accessibility was evaluated using assay of transposase accessible chromatin-sequencing. RESULTS: We stratified RA synovium into three distinct subtypes based on the hierarchical clustering of cellular composition data. One subtype was characterized by abundant HLA-DRAhigh synovial fibroblasts, autoimmune-associated B cells, GZMK+ GZMB+ CD8+ T cells, interleukin (IL)1-ß+ monocytes, and plasmablasts. In addition, tumor necrosis factor (TNF)-α, interferons (IFNs), and IL-6 signaling were highly activated in this subtype, and the expression of various chemokines was significantly enhanced. Moreover, we found an open chromatin region overlapping with RA risk locus rs9405192 near the IRF4 gene, suggesting the genetic background influences the development of this inflammatory synovial state. The other two subtypes were characterized by increased IFNs and IL-6 signaling, and expression of molecules associated with degeneration, respectively. CONCLUSION: This study adds insights into the synovial heterogeneity in East Asian patients and shows a promising link with predominant inflammatory signals. Evaluating the site of inflammation has the potential to lead to appropriate drug selection that matches the individual pathology.

Machine-learning predicts time-series prognosis factors in metastatic prostate cancer patients treated with androgen deprivation therapy.

Machine learning technology is expected to support diagnosis and prognosis prediction in medicine. We used machine learning to construct a new prognostic prediction model for prostate cancer patients based on longitudinal data obtained from age at diagnosis, peripheral blood and urine tests of 340 prostate cancer patients. Random survival forest (RSF) and survival tree were used for machine learning. In the time-series prognostic prediction model for metastatic prostate cancer patients, the RSF model showed better prediction accuracy than the conventional Cox proportional hazards model for almost all time periods of progression-free survival (PFS), overall survival (OS) and cancer-specific survival (CSS). Based on the RSF model, we created a clinically applicable prognostic prediction model using survival trees for OS and CSS by combining the values of lactate dehydrogenase (LDH) before starting treatment and alkaline phosphatase (ALP) at 120 days after treatment. Machine learning provides useful information for predicting the prognosis of metastatic prostate cancer prior to treatment intervention by considering the nonlinear and combined impacts of multiple features. The addition of data after the start of treatment would allow for more precise prognostic risk assessment of patients and would be beneficial for subsequent treatment selection.

Monitoring of blood biochemical markers for periprosthetic joint infection using ensemble machine learning and UMAP embedding.

INTRODUCTION: Periprosthetic joint infection (PJI) is a serious complication after total joint arthroplasty. It is important to accurately identify PJI and monitor postoperative blood biochemical marker changes for the appropriate treatment strategy. In this study, we aimed to monitor the postoperative blood biochemical characteristics of PJI by contrasting with non-PJI joint replacement cases to understand how the characteristics change postoperatively. MATERIALS AND METHODS: A total of 144 cases (52 of PJI and 92 of non-PJI) were reviewed retrospectively and split into development and validation cohorts. After exclusion of 11 cases, a total of 133 (PJI: 50, non-PJI: 83) cases were enrolled finally. An RF classifier was developed to discriminate between PJI and non-PJI cases based on 18 preoperative blood biochemical tests. We evaluated the similarity/dissimilarity between cases based on the RF model and embedded the cases in a two-dimensional space by Uniform Manifold Approximation and Projection (UMAP). The RF model developed based on preoperative data was also applied to the same 18 blood biochemical tests at 3, 6, and 12 months after surgery to analyze postoperative pathological changes in PJI and non-PJI. A Markov chain model was applied to calculate the transition probabilities between the two clusters after surgery. RESULTS: PJI and non-PJI were discriminated with the RF classifier with the area under the receiver operating characteristic curve of 0.778. C-reactive protein, total protein, and blood urea nitrogen were identified as the important factors that discriminates between PJI and non-PJI patients. Two clusters corresponding to the high- and low-risk populations of PJI were identified in the UMAP embedding. The high-risk cluster, which included a high proportion of PJI patients, was characterized by higher CRP and lower hemoglobin. The frequency of postoperative recurrence to the high-risk cluster was higher in PJI than in non-PJI. CONCLUSIONS: Although there was overlap between PJI and non-PJI, we were able to identify subgroups of PJI in the UMAP embedding. The machine-learning-based analytical approach is promising in consecutive monitoring of diseases such as PJI with a low incidence and long-term course.

Predicting pathological highly invasive lung cancer from preoperative [18F]FDG PET/CT with multiple machine learning models.

PURPOSE: The efficacy of sublobar resection of primary lung cancer have been proven in recent years. However, sublobar resection for highly invasive lung cancer increases local recurrence. We developed and validated multiple machine learning models predicting pathological invasiveness of lung cancer based on preoperative [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) radiomic features. METHODS: Overall, 873 patients who underwent lobectomy or segmentectomy for primary lung cancer were enrolled. Radiomics features were extracted from preoperative PET/CT images with the PyRadiomics package. Seven machine learning models and an ensemble of all models (ENS) were evaluated after 100 iterations. In addition, the probability of highly invasive lung cancer was calculated in a nested cross-validation to assess the calibration plot and clinical usefulness and to compare to consolidation tumour ratio (CTR) on CT images, one of the generally used diagnostic criteria. RESULTS: In the training set, when PET and CT features were combined, all models achieved an area under the curve (AUC) of ≥ 0.880. In the test set, ENS showed the highest mean AUC of 0.880 and smallest standard deviation of 0.0165, and when the cutoff was 0.5, accuracy of 0.804, F1 of 0.851, precision of 0.821, and recall of 0.885. In the nested cross-validation, the AUC of 0.882 (95% CI: 0.860-0.905) showed a high discriminative ability, and the calibration plot indicated consistency with a Brier score of 0.131. A decision curve analysis showed that the ENS was valid with a threshold probability ranging from 3 to 98%. Accuracy showed an improvement of more than 8% over the CTR. CONCLUSION: The machine learning model based on preoperative [18F]FDG PET/CT images was able to predict pathological highly invasive lung cancer with high discriminative ability and stability. The calibration plot showed good consistency, suggesting its usefulness in quantitative risk assessment.

Prediction of Change in Pelvic Tilt After Total Hip Arthroplasty Using Machine Learning.

BACKGROUND: A postoperative change in pelvic flexion following total hip arthroplasty (THA) is considered to be one of the causes of dislocation. This study aimed to predict the change of pelvic flexion after THA integrating preoperative and postoperative information with artificial intelligence. METHODS: This study involved 415 hips which underwent primary THA. Pelvic flexion angle (PFA) is defined as the angle created by the anterior pelvic plane and the horizontal/vertical planes in the supine/standing positions, respectively. Changes in PFA from preoperative supine position to standing position at 5 years after THA were recorded and which were defined as a 5-year change in PFA. Machine learning analysis was performed to predict 5-year change in PFA less than -20° using demographic, blood biochemical, and radiographic data as explanatory variables. Decision trees were constructed based on the important predictors for 5-year change in PFA that can be handled by humans in clinical practice. RESULTS: Among several machine learning models, random forest showed the highest accuracy (area under the curve = 0.852). Lumbo-lordotic angle, femoral anteversion angle, body mass index, pelvic tilt, and sacral slope were most important random forest predictors. By integrating these preoperative predictors with those obtained 1 year after the surgery, we developed a clinically applicable decision tree model that can predict 5-year change in PFA with area under the curve = 0.914. CONCLUSION: A machine learning model to predict 5-year change in PFA after THA has been developed by integrating preoperative and postoperative patient information, which may have capabilities for preoperative planning of THA.

Proteogenomic landscape and clinical characterization of GH-producing pituitary adenomas/somatotroph pituitary neuroendocrine tumors.

The clinical characteristics of growth hormone (GH)-producing pituitary adenomas/somatotroph pituitary neuroendocrine tumors (GHomas/somatotroph PitNETs) vary across patients. In this study, we aimed to integrate the genetic alterations, protein expression profiles, transcriptomes, and clinical characteristics of GHomas/somatotroph PitNETs to identify molecules associated with acromegaly characteristics. Targeted capture sequencing and copy number analysis of 36 genes and nontargeted proteomics analysis were performed on fresh-frozen samples from 121 sporadic GHomas/somatotroph PitNETs. Targeted capture sequencing revealed GNAS as the only driver gene, as previously reported. Classification by consensus clustering using both RNA sequencing and proteomics revealed many similarities between the proteome and the transcriptome. Gene ontology analysis was performed for differentially expressed proteins between wild-type and mutant GNAS samples identified by nontargeted proteomics and involved in G protein-coupled receptor (GPCR) pathways. The results suggested that GNAS mutations impact endocrinological features in acromegaly through GPCR pathway induction. ATP2A2 and ARID5B correlated with the GH change rate in the octreotide loading test, and WWC3, SERINC1, and ZFAND3 correlated with the tumor volume change rate after somatostatin analog treatment. These results identified a biological connection between GNAS mutations and the clinical and biochemical characteristics of acromegaly, revealing molecules associated with acromegaly that may affect medical treatment efficacy.

MicroRNA-874 targets phosphomevalonate kinase and inhibits cancer cell growth via the mevalonate pathway.

The microRNA (miR) miR-874, a potential tumour suppressor, causes cell death via target gene suppression in various cancer types. Mevalonate pathway inhibition also causes cell death in breast cancer. However, the relationship between the mevalonate pathway and miR-874-induced apoptosis or its association with the tumour suppressor p53 has not been elucidated. We identified phosphomevalonate kinase (PMVK), a key mevalonate pathway enzyme, and sterol regulatory element-binding factor 2 (SREBF2), the master cholesterol biosynthesis regulator, as direct miR­874 targets. Next-generation sequencing analysis revealed a significant miR-874-mediated downregulation of PMVK and SREBF2 gene expression and p53 pathway enrichment. Luciferase reporter assays showed that miR-874 directly regulated PMVK and SREBF2. miR-874-induced apoptosis was p53 dependent, and single-cell RNA sequencing analysis demonstrated that miR-874 transfection resulted in apoptosis and p53 pathway activation. Downregulation of PMVK expression also caused cell cycle arrest and p53 pathway activation, which was rescued by geranylgeranyl pyrophosphate (GGPP) supplementation. Analysis of The Cancer Genome Atlas (TCGA) database indicated a negative correlation between miR-874 and PMVK expression and between miR-874 and SREBF2 expression. These findings suggest that miR-874 suppresses the mevalonate pathway by targeting SREBF2 and PMVK, resulting in GGPP depletion, which activates the p53 pathway and promotes cycle arrest or apoptosis.

The Histone Methyltransferase SETD8 Regulates the Expression of Tumor Suppressor Genes via H4K20 Methylation and the p53 Signaling Pathway in Endometrial Cancer Cells.

The histone methyltransferase SET domain-containing protein 8 (SETD8), which methylates histone H4 lysine 20 (H4K20) and non-histone proteins such as p53, plays key roles in human carcinogenesis. Our aim was to determine the involvement of SETD8 in endometrial cancer and its therapeutic potential and identify the downstream genes regulated by SETD8 via H4K20 methylation and the p53 signaling pathway. We examined the expression profile of SETD8 and evaluated whether SETD8 plays a critical role in the proliferation of endometrial cancer cells using small interfering RNAs (siRNAs). We identified the prognostically important genes regulated by SETD8 via H4K20 methylation and p53 signaling using chromatin immunoprecipitation sequencing, RNA sequencing, and machine learning. We confirmed that SETD8 expression was elevated in endometrial cancer tissues. Our in vitro results suggest that the suppression of SETD8 using siRNA or a selective inhibitor attenuated cell proliferation and promoted the apoptosis of endometrial cancer cells. In these cells, SETD8 regulates genes via H4K20 methylation and the p53 signaling pathway. We also identified the prognostically important genes related to apoptosis, such as those encoding KIAA1324 and TP73, in endometrial cancer. SETD8 is an important gene for carcinogenesis and progression of endometrial cancer via H4K20 methylation.

Diagnosis of Atrial Fibrillation Using Machine Learning With Wearable Devices After Cardiac Surgery: Algorithm Development Study.

BACKGROUND: Some attempts have been made to detect atrial fibrillation (AF) with a wearable device equipped with photoelectric volumetric pulse wave technology, and it is expected to be applied under real clinical conditions. OBJECTIVE: This study is the second part of a 2-phase study aimed at developing a method for immediate detection of paroxysmal AF, using a wearable device with built-in photoplethysmography (PPG). The objective of this study is to develop an algorithm to immediately diagnose AF by an Apple Watch equipped with a PPG sensor that is worn by patients undergoing cardiac surgery and to use machine learning on the pulse data output from the device. METHODS: A total of 80 patients who underwent cardiac surgery at a single institution between June 2020 and March 2021 were monitored for postoperative AF, using a telemetry-monitored electrocardiogram (ECG) and an Apple Watch. AF was diagnosed by qualified physicians from telemetry-monitored ECGs and 12-lead ECGs; a diagnostic algorithm was developed using machine learning on the pulse rate data output from the Apple Watch. RESULTS: One of the 80 patients was excluded from the analysis due to redness caused by wearing the Apple Watch. Of 79 patients, 27 (34.2%) developed AF, and 199 events of AF including brief AF were observed. Of them, 18 events of AF lasting longer than 1 hour were observed, and cross-correlation analysis showed that pulse rate measured by Apple Watch was strongly correlated (cross-correlation functions [CCF]: 0.6-0.8) with 8 events and very strongly correlated (CCF>0.8) with 3 events. The diagnostic accuracy by machine learning was 0.9416 (sensitivity 0.909 and specificity 0.838 at the point closest to the top left) for the area under the receiver operating characteristic curve. CONCLUSIONS: We were able to safely monitor pulse rate in patients who wore an Apple Watch after cardiac surgery. Although the pulse rate measured by the PPG sensor does not follow the heart rate recorded by telemetry-monitored ECGs in some parts, which may reduce the accuracy of AF diagnosis by machine learning, we have shown the possibility of clinical application of using only the pulse rate collected by the PPG sensor for the early detection of AF.

Population-based Screening for Hereditary Colorectal Cancer Variants in Japan.

BACKGROUND & AIMS: Colorectal cancer (CRC) is one of the most common cancers in the world. A small proportion of CRCs can be attributed to recognizable hereditary germline variants of known CRC susceptibility genes. To better understand cancer risk, it is necessary to explore the prevalence of hereditary CRC and pathogenic variants of multiple cancer-predisposing genes in non-European populations. METHODS: We analyzed the coding regions of 27 cancer-predisposing genes in 12,503 unselected Japanese CRC patients and 23,705 controls by target sequencing and genome-wide SNP chip. Their clinical significance was assessed using ClinVar and the guidelines by ACMG/AMP. RESULTS: We identified 4,804 variants in the 27 genes and annotated them as pathogenic in 397 and benign variants in 941, of which 43.6% were novel. In total, 3.3% of the unselected CRC patients and 1.5% of the controls had a pathogenic variant. The pathogenic variants of MSH2 (odds ratio (OR) = 18.1), MLH1 (OR = 8.6), MSH6 (OR = 4.9), APC (OR = 49.4), BRIP1 (OR=3.6), BRCA1 (OR = 2.6), BRCA2 (OR = 1.9), and TP53 (OR = 1.7) were significantly associated with CRC development in the Japanese population (P-values<0.01, FDR<0.05). These pathogenic variants were significantly associated with diagnosis age and personal/family history of cancer. In total, at least 3.5% of the Japanese CRC population had a pathogenic variant or CNV of the 27 cancer-predisposing genes, indicating hereditary cancers. CONCLUSIONS: This largest study of CRC heredity in Asia can contribute to the development of guidelines for genetic testing and variant interpretation for heritable CRCs.

Mitochondrial reactive oxygen species trigger metformin-dependent antitumor immunity via activation of Nrf2/mTORC1/p62 axis in tumor-infiltrating CD8T lymphocytes.

BACKGROUND: Metformin (Met) is the first-line treatment for type 2 diabetes mellitus and plays an effective role in treating various diseases, such as cardiovascular disease, neurodegenerative disease, cancer, and aging. However, the underlying mechanism of Met-dependent antitumor immunity remains to be elucidated. METHODS: MitoTEMPO, a scavenger of mitochondrial superoxide, abolished the antitumor effect of Met, but not antiprogrammed cell death (PD-1) antibody (Ab) treatment. Consequently, we studied the mechanism of the Met-induced antitumor effect. Expressions of glucose transporter (Glut)-1, mitochondrial reactive oxygen species (mtROS), interferon (IFN)-γ, Ki67, autophagy markers, activation markers for NF-E2-related factor 2 (Nrf2), and mammalian target of rapamaycin complex 1 (mTORC1) in CD8+ tumor-infiltrating T lymphocytes (CD8TILs) were examined by flow cytometry analysis. In addition, conditional knockout mice for Nrf2 and p62 were used to detect these markers, together with the monitoring of in vivo tumor growth. RNA sequencing was performed for CD8TILs and tumor cells. Melanoma cells containing an IFN-γ receptor (IFNγR) cytoplasmic domain deletion mutant was overexpressed and used for characterization of the metabolic profile of those tumor cells using a Seahorse Flux Analyzer. RESULTS: Met administration elevates mtROS and cell surface Glut-1, resulting in the production of IFN-γ in CD8TILs. mtROS activates Nrf2 in a glycolysis-dependent manner, inducing activation of autophagy, glutaminolysis, mTORC1, and p62/SQSTM1. mTORC1-dependent phosphorylation of p62 at serine 351 (p-p62(S351)) is also involved in activation of Nrf2. Conditional deletion of Nrf2 in CD8TILs abrogates mTORC1 activation and antitumor immunity by Met. In synergy with the effect of anti-PD-1 Ab, Met boosts CD8TIL proliferation and IFN-γ secretion, resulting in decreased glycolysis and oxidative phosphorylation in tumor cells. Consequently, Glut-1 is elevated in CD8TILs, together with the expansion of activated dendritic cells. Moreover, tumor cells lacking in IFNγR signaling abolish IFN-γ production and proliferation of CD8TILs. CONCLUSIONS: We found that Met stimulates production of mtROS, which triggers Glut-1 elevation and Nrf2 activation in CD8TILs. Nrf2 activates mTORC1, whereas mTORC1 activates Nrf2 in a p-p62(S351)-dependent manner, thus creating a feedback loop that ensures CD8TILs' proliferation. In combination with anti-PD-1 Ab, Met stimulates robust proliferation of CD8TILs and IFN-γ secretion, resulting in an IFN-γ-dependent reprogramming of the tumor microenvironment.

Targeting critical kinases and anti-apoptotic molecules overcomes steroid resistance in MLL-rearranged leukaemia.

BACKGROUND: Acute lymphoblastic leukaemia with mixed lineage leukaemia gene rearrangement (MLL-ALL) frequently affects infants and is associated with a poor prognosis. Primary refractory and relapsed disease due to resistance to glucocorticoids (GCs) remains a substantial hurdle to improving clinical outcomes. In this study, we aimed to overcome GC resistance of MLL-ALL. METHODS: Using leukaemia patient specimens, we performed bioinformatic analyses to identify target genes/pathways. To test inhibition of target pathways in vivo, we created pre-clinical therapeutic mouse patient-derived xenograft (PDX)-models by transplanting human MLL-ALL leukaemia initiating cells (LIC) into immune-deficient NSG mice. Finally, we conducted B-cell lymphoma-2 (BCL-2) homology domain 3 (BH3) profiling to identify BH3 peptides responsible for treatment resistance in MLL-leukaemia. FINDINGS: Src family kinases (SFKs) and Fms-like tyrosine kinase 3 (FLT3) signaling pathway were over-represented in MLL-ALL cells. PDX-models of infant MLL- ALL recapitulated GC-resistance in vivo but RK-20449, an inhibitor of SFKs and FLT3 eliminated human MLL-ALL cells in vivo, overcoming GC-resistance. Further, we identified BCL-2 dependence as a mechanism of treatment resistance in MLL-ALL through BH3 profiling. Furthermore, MLL-ALL cells resistant to RK-20449 treatment were dependent on the anti-apoptotic BCL-2 protein for their survival. Combined inhibition of SFKs/FLT3 by RK-20449 and of BCL-2 by ABT-199 led to substantial elimination of MLL-ALL cells in vitro and in vivo. Triple treatment combining GCs, RK-20449 and ABT-199 resulted in complete elimination of MLL-ALL cells in vivo. INTERPRETATION: SFKs/FLT3 signaling pathways are promising targets for treatment of treatment-resistant MLL-ALL. Combined inhibition of these kinase pathways and anti-apoptotic BCL-2 successfully eliminated highly resistant MLL-ALL and demonstrated a new treatment strategy for treatment-resistant poor-outcome MLL-ALL. FUNDING: This study was supported by RIKEN (RIKEN President's Discretionary Grant) for FI, Japan Agency for Medical Research and Development (the Basic Science and Platform Technology Program for Innovative Biological Medicine for FI and by NIH CA034196 for LDS. The funders had no role in the study design, data collection, data analysis, interpretation nor writing of the report.

Exit from germinal center to become quiescent memory B cells depends on metabolic reprograming and provision of a survival signal.

A still unanswered question is what drives the small fraction of activated germinal center (GC) B cells to become long-lived quiescent memory B cells. We found here that a small population of GC-derived CD38intBcl6hi/intEfnb1+ cells with lower mTORC1 activity favored the memory B cell fate. Constitutively high mTORC1 activity led to defects in formation of the CD38intBcl6hi/intEfnb1+ cells; conversely, decreasing mTORC1 activity resulted in relative enrichment of this memory-prone population over the recycling-prone one. Furthermore, the CD38intBcl6hi/intEfnb1+ cells had higher levels of Bcl2 and surface BCR that, in turn, contributed to their survival and development. We also found that downregulation of Bcl6 resulted in increased expression of both Bcl2 and BCR. Given the positive correlation between the strength of T cell help and mTORC1 activity, our data suggest a model in which weak help from T cells together with provision of an increased survival signal are key for GC B cells to adopt a memory B cell fate.

[Health and Medical Revolution Driven by Artificial Intelligence].

With the development and diversification of medical care, the importance of precision medicine, which selects a suitable treatment for the individual patient from a huge number of options, is increasing. It is often difficult to explain multifactorial diseases such as cancer and chronic inflammatory diseases by a single hypothesis. In such case, a data-driven approach is essential to construct individualized models based on comprehensive observation of the target disease. The data-driven approach utilizes artificial intelligence to extract, predict, and classify patterns of data, considering different types of variables and complex dependencies between variables. In this paper, we introduce the basic idea, typical methods, and application examples of artificial intelligence and its core technology, machine learning. We would like to discuss a new framework of medical research toward the next generation medicine, while reviewing how machine learning is used in precise prediction and data-driven redefinition of diseases.

Genetic characterization of pancreatic cancer patients and prediction of carrier status of germline pathogenic variants in cancer-predisposing genes.

BACKGROUND: National Comprehensive Cancer Network (NCCN) recently recommended germline genetic testing for all pancreatic cancer patients. However, the genes targeted by genetic testing and the feasibility of selecting patients likely to carry pathogenic variants have not been sufficiently verified. The purpose of this study was to genetically characterize Japanese patients and examine whether the current guideline is applicable in this population. METHODS: Using targeted sequencing, we analyzed the coding regions of 27 cancer-predisposing genes in 1,005 pancreatic cancer patients and 23,705 controls in Japan. We compared the pathogenic variant frequency between cases and controls and documented the demographic and clinical characteristics of carrier patients. We then examined if it was possible to use machine learning to predict carrier status based on those characteristics. FINDINGS: We identified 205 pathogenic variants across the 27 genes. Pathogenic variants in BRCA2, ATM, and BRCA1 were significantly associated with pancreatic cancer. Characteristics associated with carrier status were inconsistent with previous investigations. Machine learning classifiers had a low performance in determining the carrier status of pancreatic cancer patients, while the same classifiers, when applied to breast cancer data as a positive control, had a higher performance that was comparable to that of the NCCN guideline. INTERPRETATION: Our findings support the clinical significance of multigene panel testing for pancreatic cancer and indicate that at least 3.4% of Japanese patients may respond to poly (ADP ribose) polymerase inhibitor treatments. The difficulty in predicting carrier status suggests that offering germline genetic testing for all pancreatic cancer patients is reasonable. FUNDING: AMED under Grant Number JP19kk0305010 and Australian National Health and Medical Research funding (ID177524).

Multiomics Investigation Revealing the Characteristics of HIV-1-Infected Cells In Vivo.

For eradication of HIV-1 infection, it is important to elucidate the detailed features and heterogeneity of HIV-1-infected cells in vivo. To reveal multiple characteristics of HIV-1-producing cells in vivo, we use a hematopoietic-stem-cell-transplanted humanized mouse model infected with GFP-encoding replication-competent HIV-1. We perform multiomics experiments using recently developed technology to identify the features of HIV-1-infected cells. Genome-wide HIV-1 integration-site analysis reveals that productive HIV-1 infection tends to occur in cells with viral integration into transcriptionally active genomic regions. Bulk transcriptome analysis reveals that a high level of viral mRNA is transcribed in HIV-1-infected cells. Moreover, single-cell transcriptome analysis shows the heterogeneity of HIV-1-infected cells, including CXCL13high cells and a subpopulation with low expression of interferon-stimulated genes, which can contribute to efficient viral spread in vivo. Our findings describe multiple characteristics of HIV-1-producing cells in vivo, which could provide clues for the development of an HIV-1 cure.

Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases.

The overwhelming majority of participants in current genetic studies are of European ancestry. To elucidate disease biology in the East Asian population, we conducted a genome-wide association study (GWAS) with 212,453 Japanese individuals across 42 diseases. We detected 320 independent signals in 276 loci for 27 diseases, with 25 novel loci (P < 9.58 × 10-9). East Asian-specific missense variants were identified as candidate causal variants for three novel loci, and we successfully replicated two of them by analyzing independent Japanese cohorts; p.R220W of ATG16L2 (associated with coronary artery disease) and p.V326A of POT1 (associated with lung cancer). We further investigated enrichment of heritability within 2,868 annotations of genome-wide transcription factor occupancy, and identified 378 significant enrichments across nine diseases (false discovery rate < 0.05) (for example, NKX3-1 for prostate cancer). This large-scale GWAS in a Japanese population provides insights into the etiology of complex diseases and highlights the importance of performing GWAS in non-European populations.

Tet2 and Tet3 in B cells are required to repress CD86 and prevent autoimmunity.

A contribution of epigenetic modifications to B cell tolerance has been proposed but not directly tested. Here we report that deficiency of ten-eleven translocation (Tet) DNA demethylase family members Tet2 and Tet3 in B cells led to hyperactivation of B and T cells, autoantibody production and lupus-like disease in mice. Mechanistically, in the absence of Tet2 and Tet3, downregulation of CD86, which normally occurs following chronic exposure of self-reactive B cells to self-antigen, did not take place. The importance of dysregulated CD86 expression in Tet2- and Tet3-deficient B cells was further demonstrated by the restriction, albeit not complete, on aberrant T and B cell activation following anti-CD86 blockade. Tet2- and Tet3-deficient B cells had decreased accumulation of histone deacetylase 1 (HDAC1) and HDAC2 at the Cd86 locus. Thus, our findings suggest that Tet2- and Tet3-mediated chromatin modification participates in repression of CD86 on chronically stimulated self-reactive B cells, which contributes, at least in part, to preventing autoimmunity.